Paper Citation Record · LEDGER

How Do Large Language Monkeys Get Their Power (Laws)?

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2502.17578.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.17578 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:06:38.402486Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ac143d01-b5cc-471d-87fb-70751c3cedd8 · inbound

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models cites this paper.

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models How Do Large Language Monkeys Get Their Power (Laws)?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:31:56.210081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3642f3b-331d-4dbb-a3f4-9324325275fb · inbound

RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models cites this paper.

RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models How Do Large Language Monkeys Get Their Power (Laws)?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:38.402486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4d4a8e2-f39f-487d-8481-bd0bef63980b · inbound

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track cites this paper.

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track How Do Large Language Monkeys Get Their Power (Laws)?

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:20.708466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c202173f-6b10-4037-a45a-dd386d86f6d4 · inbound

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation cites this paper.

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation How Do Large Language Monkeys Get Their Power (Laws)?

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:06:13.869059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b83a271b-ea82-4629-8ac5-c33dacdc6dc5 · inbound

Probabilistic Programs of Thought cites this paper.

Probabilistic Programs of Thought How Do Large Language Monkeys Get Their Power (Laws)?

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:51:10.208956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7a8edaae-f8a0-4769-a733-43c4fb1929bf · inbound

Characterizing Model-Native Skills cites this paper.

Characterizing Model-Native Skills How Do Large Language Monkeys Get Their Power (Laws)?

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T06:06:19.553634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ade06ac-bc2f-4ecb-8261-8ce65db10d6a · inbound

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cites this paper.

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space How Do Large Language Monkeys Get Their Power (Laws)?

Reference 97

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:22:18.875764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80680253-3a04-4778-a1eb-4c4cd29eeaa2 · inbound

An Asymptotic Theory of Chain-of-Thought in In-Context Learning cites this paper.

An Asymptotic Theory of Chain-of-Thought in In-Context Learning How Do Large Language Monkeys Get Their Power (Laws)?

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:16:38.973242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6951b9b8-b898-4975-9f78-d6382194754d · inbound

Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation cites this paper.

Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation How Do Large Language Monkeys Get Their Power (Laws)?

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T22:52:45.587470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ddd0f676-3ad8-4da3-b7e4-4c7f55b744cd · inbound

When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling cites this paper.

When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling How Do Large Language Monkeys Get Their Power (Laws)?

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:44:37.099865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55bf7bc1-b5d4-46bc-886b-1901e31a04e2 · inbound

Two AI Metrics Diverged: Will it Make All the Difference? cites this paper.

Two AI Metrics Diverged: Will it Make All the Difference? How Do Large Language Monkeys Get Their Power (Laws)?

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:36:56.112374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.